Analyzing complex single molecule emission patterns with deep learning

A fluorescent emitter simultaneously transmits its identity, location, and cellular context through its emission pattern. We developed smNet, a deep neural network for multiplexed single-molecule analysis to enable retrieving such information with high accuracy. We demonstrate that smNet can extract...

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Detalles Bibliográficos
Autores principales: Zhang, Peiyi, Liu, Sheng, Chaurasia, Abhishek, Ma, Donghan, Mlodzianoski, Michael J., Culurciello, Eugenio, Huang, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6624853/
https://www.ncbi.nlm.nih.gov/pubmed/30377349
http://dx.doi.org/10.1038/s41592-018-0153-5
Descripción
Sumario:A fluorescent emitter simultaneously transmits its identity, location, and cellular context through its emission pattern. We developed smNet, a deep neural network for multiplexed single-molecule analysis to enable retrieving such information with high accuracy. We demonstrate that smNet can extract three-dimensional molecule location, orientation, and wavefront distortion with precision approaching the theoretical limit and therefore will allow multiplexed measurements through the emission pattern of a single molecule.

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